Set up
suppressPackageStartupMessages({
library(tidyverse)
})
Read in data and process
tmb_vaf_df <- readr::read_tsv(tmb_vaf_file, guess_max = 100000, show_col_types = FALSE) %>%
filter(!tmb >= 10) %>%
select(Kids_First_Biospecimen_ID, Variant_Classification, gene_protein, mutation_count, region_size, tmb, VAF)
genomic_paired_df <- readr::read_tsv(genomic_paired_file, guess_max = 100000, show_col_types = FALSE) %>%
left_join(tmb_vaf_df, by = c(\Kids_First_Biospecimen_ID\)) %>%
filter(!is.na(tmb))
# Attention as some bs specimen might not have TMB!
# If that happens, we will end up with samples lacking timepoints.
# Which patient samples don't have TMB?
# genomic_paired_df %>%
# filter(is.na(tmb)) %>%
# unique() %>%
# regulartable() %>%
# fontsize(size = 12, part = \all\)
descriptors_df <- genomic_paired_df %>%
group_by(Kids_First_Participant_ID) %>%
summarize(descriptors = paste(sort(tumor_descriptor), collapse = \
# Read color palette
palette_df <- readr::read_tsv(palette_file, guess_max = 100000, show_col_types = FALSE)
# Define and order palette
palette <- palette_df$hex_codes
names(palette) <- palette_df$Variant_Classification
# Define label for plots
Alteration_type <- df_plot$Variant_Classification
# Define ylim
ylim <- max(df_plot$log10_tmb)
What type of alterations we observe per tumor descriptor?
# Create bxp
print(ggpubr::ggboxplot(df_plot,
x = \tumor_descriptor\,
y = \log10_tmb\,
color = \Variant_Classification\,
palette = palette) +
theme_Publication() +
scale_y_continuous(limits = c(0, ylim)) +
xlab(\Timepoint\) +
theme(axis.text.x = element_text(angle = 90)))

# Save the plot
ggsave(filename = \Alteration_type_timepoints.pdf\,
path = plots_dir,
width = 15,
height = 8,
device = \pdf\,
useDingbats = FALSE)
What type of alterations we observe per tumor descriptor in each
cancer group?
# Create bxp
print(ggpubr::ggboxplot(df_plot,
x = \tumor_descriptor\,
y = \log10_tmb\,
color = \Variant_Classification\,
palette = palette) +
facet_wrap(~cg_id) +
theme_Publication() +
xlab(\Timepoint\) +
scale_y_continuous(limits = c(0, ylim)) +
theme(axis.text.x = element_text(angle = 90)))

# Save the plot
ggsave(filename = \Alteration_type_cancer_group.pdf\,
path = plots_dir,
width = 25,
height = 18,
device = \pdf\,
useDingbats = FALSE)
What type of alterations we observe per tumor descriptor in each
cancer group defined by cgGFAC?
df_plot_cgGFAC <- df_plot %>%
arrange(tumor_descriptor_cgGFAC_n)
#mutate(tumor_descriptor_cgGFAC_n = factor(tumor_descriptor_cgGFAC_n))
#df_plot_cgGFAC$tumor_descriptor_cgGFAC_n %>% levels()
# Create bxp
print(ggpubr::ggboxplot(df_plot_cgGFAC,
x = \tumor_descriptor_cgGFAC_n\,
y = \log10_tmb\,
color = \Variant_Classification\,
palette = palette) +
theme_Publication() +
xlab(\Timepoint\) +
scale_y_continuous(limits = c(0, ylim)) +
theme(axis.text.x = element_text(angle = 90)))

# Save the plot
ggsave(filename = \Alteration_type_cgGFAC.pdf\,
path = plots_dir,
width = 14,
height = 8,
device = \pdf\,
useDingbats = FALSE)
cgGFAC_id <- as.character(unique(df_plot_cgGFAC$cgGFAC))
cgGFAC_id
[1] \ATRT\ \DMG\ \HGG\ \LGG\ \Other\
ATRT
DMG
HGG
LGG
Other
# Loop through variable
for (i in seq_along(cgGFAC_id)){
print(i)
df_sub <- df_plot_cgGFAC %>%
filter(cgGFAC == cgGFAC_id[i])
# Create bxp
print(ggpubr::ggboxplot(df_sub,
x = \tumor_descriptor_cgGFAC_n\,
y = \log10_tmb\,
color = \Variant_Classification\,
palette = palette) +
theme_Publication() +
labs(title = paste(cgGFAC_id[i])) +
scale_y_continuous(limits = c(0, ylim)) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)))
}
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What type of alterations we observe per tumor descriptor in each
cancer group (add _n))?
cg <- as.character(unique(df_plot$cg_id))
cg
[1] \Embryonal tumor with multilayer rosettes\
[2] \Low-grade glioma\
[3] \Ependymoma\
[4] \Medulloblastoma\
[5] \Atypical Teratoid Rhabdoid Tumor\
[6] \Diffuse midline glioma\
[7] \High-grade glioma\
[8] \Ganglioglioma\
[9] \Meningioma\
[10] \Pilocytic astrocytoma\
[11] \CNS Embryonal tumor\
[12] \Neuroblastoma\
[13] \Schwannoma\
[14] \Chordoma\
[15] \Malignant peripheral nerve sheath tumor\
[16] \Choroid plexus carcinoma\
[17] \Adamantinomatous Craniopharyngioma\
[18] \Dysembryoplastic neuroepithelial tumor\
[19] \Ewing sarcoma\
[20] \Rosai-Dorfman disease\
[21] \Neurofibroma/Plexiform\
[22] \Glial-neuronal tumor\
[23] \Hemangioblastoma\
[24] \Craniopharyngioma\
Embryonal tumor with multilayer rosettes
Low-grade glioma
Ependymoma
Medulloblastoma
Atypical Teratoid Rhabdoid Tumor
Diffuse midline glioma
High-grade glioma
Ganglioglioma
Meningioma
Pilocytic astrocytoma
CNS Embryonal tumor
Neuroblastoma
Schwannoma
Chordoma
Malignant peripheral nerve sheath tumor
Choroid plexus carcinoma
Adamantinomatous Craniopharyngioma
Dysembryoplastic neuroepithelial tumor
Ewing sarcoma
Rosai-Dorfman disease
Neurofibroma/Plexiform
Glial-neuronal tumor
Hemangioblastoma
Craniopharyngioma
# Loop through variable
for (i in seq_along(cg)){
print(i)
df_sub <- df_plot %>%
filter(cg_id == cg[i])
# Create bxp
print(ggpubr::ggboxplot(df_sub,
x = \tumor_descriptor_cg_n\,
y = \log10_tmb\,
color = \Variant_Classification\,
palette = palette) +
theme_Publication() +
xlab(\Timepoint\) +
labs(title = paste(cg[i])) +
scale_y_continuous(limits = c(0, ylim)) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)))
}
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What type of alterations we observe per tumor descriptor in each
cancer group and timepoint model?
tm <- as.character(unique(df_plot$timepoints_models))
tm
[1] \Dx-Rec\ \Dx-Pro\ \Dx-Dec\ \Pro-Dec\
[5] \Rec-SM\ \Pro-Rec\ \Dx-SM\ \Pro-Rec-Dec\
[9] \Dx-Pro-Rec\ \Rec-Dec\ \Dx-Pro-Rec-Dec\ \Dx-Rec-Dec\
[13] \Dx-Pro-Dec\
Dx-Rec
Dx-Pro
Dx-Dec
Pro-Dec
Rec-SM
Pro-Rec
Dx-SM
Pro-Rec-Dec
Dx-Pro-Rec
Rec-Dec
Dx-Pro-Rec-Dec
Dx-Rec-Dec
Dx-Pro-Dec
# Loop through variable
for (i in seq_along(tm)){
print(i)
df_sub <- df_plot %>%
filter(timepoints_models == tm[i])
# Create bxp
print(ggpubr::ggboxplot(df_sub,
x = \tumor_descriptor\,
y = \tmb\,
color = \Variant_Classification\,
palette = palette) +
facet_wrap(~cancer_group) +
theme_Publication() +
ylab(\TMB\) +
xlab(\Timepoint\) +
scale_y_continuous(limits = c(0, ylim)) +
theme(axis.text.x = element_text(angle = 90)))
}
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sessionInfo()
R version 4.2.3 (2023-03-15)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.5.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ggthemes_4.2.4 lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[5] dplyr_1.1.2 purrr_1.0.1 readr_2.1.4 tidyr_1.3.0
[9] tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] tidyselect_1.2.0 xfun_0.39 bslib_0.5.0 carData_3.0-5
[5] colorspace_2.1-0 vctrs_0.6.3 generics_0.1.3 htmltools_0.5.5
[9] yaml_2.3.7 utf8_1.2.3 rlang_1.1.1 jquerylib_0.1.4
[13] pillar_1.9.0 ggpubr_0.6.0 glue_1.6.2 withr_2.5.0
[17] bit64_4.0.5 lifecycle_1.0.3 munsell_0.5.0 ggsignif_0.6.4
[21] gtable_0.3.3 ragg_1.2.5 evaluate_0.21 labeling_0.4.2
[25] knitr_1.43 tzdb_0.4.0 fastmap_1.1.1 parallel_4.2.3
[29] fansi_1.0.4 highr_0.10 broom_1.0.5 scales_1.2.1
[33] backports_1.4.1 cachem_1.0.8 vroom_1.6.3 jsonlite_1.8.7
[37] abind_1.4-5 systemfonts_1.0.4 farver_2.1.1 bit_4.0.5
[41] textshaping_0.3.6 hms_1.1.3 digest_0.6.33 stringi_1.7.12
[45] rstatix_0.7.2 rprojroot_2.0.3 cli_3.6.1 tools_4.2.3
[49] magrittr_2.0.3 sass_0.4.7 crayon_1.5.2 car_3.1-2
[53] pkgconfig_2.0.3 timechange_0.2.0 rmarkdown_2.23 R6_2.5.1
[57] compiler_4.2.3
---
title: "Classification of Variants across paired longitudinal samples in the PBTA Cohort"
author: 'Antonia Chroni <chronia@chop.edu> for D3B'
date: "2023"
output:
  html_notebook:
    toc: TRUE
    toc_float: TRUE
---

# Set up
```{r load-library}
suppressPackageStartupMessages({
  library(tidyverse)
})
```

## Directories and File Inputs/Outputs
```{r set-dir-and-file-names}
# Detect the ".git" folder -- this will be in the project root directory
# Use this as the root directory to ensure proper sourcing of functions 
# no matter where this is called from
root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))
analysis_dir <- file.path(root_dir, "analyses", "tmb-vaf-longitudinal")
results_dir <- file.path(analysis_dir, "results")
input_dir <- file.path(analysis_dir, "input")
files_dir <- file.path(root_dir, "analyses", "sample-distribution-analysis", "results")

# Input files
genomic_paired_file <- file.path(files_dir, "genomic_assays_matched_time_points_extended.tsv") 
tmb_vaf_file <- file.path(results_dir, "tmb_vaf_genomic.tsv")
palette_file <- file.path(root_dir, "figures", "palettes", "oncoprint_color_palette.tsv")

# File path to plot directory
plots_dir <-
  file.path(analysis_dir, "plots")
if (!dir.exists(plots_dir)) {
  dir.create(plots_dir)
}

source(paste0(root_dir, "/figures/scripts/theme.R"))
```

## Read in data and process
```{r load-process-inputs}
tmb_vaf_df <- readr::read_tsv(tmb_vaf_file, guess_max = 100000, show_col_types = FALSE) %>% 
  filter(!tmb >= 10) %>% 
  select(Kids_First_Biospecimen_ID, Variant_Classification, gene_protein, mutation_count,	region_size, tmb, VAF)

genomic_paired_df <- readr::read_tsv(genomic_paired_file, guess_max = 100000, show_col_types = FALSE) %>% 
  left_join(tmb_vaf_df, by = c("Kids_First_Biospecimen_ID")) %>%
  filter(!is.na(tmb)) 


# Attention as some bs specimen might not have TMB!
# If that happens, we will end up with samples lacking timepoints.

# Which patient samples don't have TMB?
# genomic_paired_df %>% 
#  filter(is.na(tmb)) %>% 
#  unique() %>% 
#  regulartable() %>%
#  fontsize(size = 12, part = "all")

descriptors_df <- genomic_paired_df %>%
  group_by(Kids_First_Participant_ID) %>%
  summarize(descriptors = paste(sort(tumor_descriptor), collapse = ", "),) 


# Vector to order timepoints
timepoints <- c("Diagnosis", "Progressive", "Recurrence", "Deceased", "Second Malignancy", "Unavailable")

df <- genomic_paired_df %>% 
  left_join(descriptors_df, by = c("Kids_First_Participant_ID")) %>% 
  mutate(cgGFAC = case_when(grepl("High-grade glioma", cancer_group) ~ "HGG",
                            grepl("Diffuse midline glioma", cancer_group) ~ "DMG",
                            grepl("Atypical Teratoid Rhabdoid Tumor", cancer_group) ~ "ATRT",
                            grepl("Low-grade glioma", cancer_group) ~ "LGG",
                            TRUE ~ "Other"),
         td_cgGFAC = case_when(grepl("Deceased", tumor_descriptor) ~ "xDeceased",
                                      TRUE ~ tumor_descriptor),
         log10_tmb = abs(log10(tmb)))

# Let's count #samples per cancer groups and timepoints.
# We will use the cg_id col that indicates cancer type as identified at the first diagnostic sample
timepoint_cg_n_df <- df %>% 
  count(cg_id, tumor_descriptor) %>% 
  dplyr::mutate(tumor_descriptor_cg_n = glue::glue("{cg_id}_{tumor_descriptor}  (N={n})")) %>% 
  dplyr::rename(timepoint_cg_n = n) 

# Let's count #samples per cancer groups and timepoints 
timepoint_cgGFAC_n_df <- df %>% 
  count(cgGFAC, td_cgGFAC) %>% 
  dplyr::mutate(tumor_descriptor_cgGFAC_n = glue::glue("{cgGFAC}_{td_cgGFAC}  (N={n})")) %>% 
  dplyr::rename(timepoint_cgGFAC_n = n) 

# Create df to use for plots
df_plot <- df %>% 
  left_join(timepoint_cg_n_df, by = c("tumor_descriptor", "cg_id")) %>%
  left_join(timepoint_cgGFAC_n_df, by = c("td_cgGFAC", "cgGFAC")) %>% 
  filter(!timepoint_cg_n <= 2,
         !timepoint_cgGFAC_n <= 2,
         !cg_id == "NA") %>% 
  mutate(tumor_descriptor = factor(tumor_descriptor),
         tumor_descriptor = fct_relevel(tumor_descriptor, timepoints))
        
``` 


```{r define-parameters-for-plots}
# Read color palette
palette_df <- readr::read_tsv(palette_file, guess_max = 100000, show_col_types = FALSE) 

# Define and order palette
palette <- palette_df$hex_codes
names(palette) <- palette_df$Variant_Classification

# Define label for plots
Alteration_type <- df_plot$Variant_Classification

# Define ylim
ylim <- max(df_plot$log10_tmb)
```

# What type of alterations we observe per tumor descriptor?

```{r plot-timepoint, fig.width = 15, fig.height = 8, fig.fullwidth = TRUE}
# Create bxp
print(ggpubr::ggboxplot(df_plot, 
                        x = "tumor_descriptor", 
                        y = "log10_tmb", 
                        color = "Variant_Classification",
                        palette = palette) +
        theme_Publication() + 
        scale_y_continuous(limits = c(0, ylim)) +
        xlab("Timepoint") +
        theme(axis.text.x = element_text(angle = 90)))

# Save the plot
ggsave(filename = "Alteration_type_timepoints.pdf", 
       path = plots_dir, 
       width = 15, 
       height = 8, 
       device = "pdf", 
       useDingbats = FALSE)
```


# What type of alterations we observe per tumor descriptor in each cancer group?

```{r plot-cg-id, fig.width = 25, fig.height = 18, fig.fullwidth = TRUE}
# Create bxp
print(ggpubr::ggboxplot(df_plot, 
                        x = "tumor_descriptor", 
                        y = "log10_tmb", 
                        color = "Variant_Classification",
                        palette = palette) +
        facet_wrap(~cg_id) +
        theme_Publication() + 
        xlab("Timepoint") +
        scale_y_continuous(limits = c(0, ylim)) +
        theme(axis.text.x = element_text(angle = 90)))

# Save the plot
ggsave(filename = "Alteration_type_cancer_group.pdf", 
       path = plots_dir, 
       width = 25, 
       height = 18, 
       device = "pdf", 
       useDingbats = FALSE)
```


# What type of alterations we observe per tumor descriptor in each cancer group defined by cgGFAC?

```{r plot-cgGFAC-n, fig.width = 14, fig.height = 8, fig.fullwidth = TRUE}
df_plot_cgGFAC <- df_plot %>% 
  arrange(tumor_descriptor_cgGFAC_n)
  #mutate(tumor_descriptor_cgGFAC_n = factor(tumor_descriptor_cgGFAC_n)) 

#df_plot_cgGFAC$tumor_descriptor_cgGFAC_n %>% levels()

# Create bxp
print(ggpubr::ggboxplot(df_plot_cgGFAC, 
                        x = "tumor_descriptor_cgGFAC_n", 
                        y = "log10_tmb", 
                        color = "Variant_Classification",
                        palette = palette) +
        theme_Publication() + 
        xlab("Timepoint") +
        scale_y_continuous(limits = c(0, ylim)) +
        theme(axis.text.x = element_text(angle = 90)))

# Save the plot
ggsave(filename = "Alteration_type_cgGFAC.pdf", 
       path = plots_dir, 
       width = 14, 
       height = 8, 
       device = "pdf", 
       useDingbats = FALSE)

```


```{r plot-cgGFAC-n-individual-plots, fig.width = 8, fig.height = 6, fig.fullwidth = TRUE}
cgGFAC_id <- as.character(unique(df_plot_cgGFAC$cgGFAC))
cgGFAC_id

# Loop through variable
for (i in seq_along(cgGFAC_id)){
  print(i)
  df_sub <- df_plot_cgGFAC %>%
      filter(cgGFAC == cgGFAC_id[i])

  
   # Create bxp
  print(ggpubr::ggboxplot(df_sub, 
                        x = "tumor_descriptor_cgGFAC_n", 
                        y = "log10_tmb", 
                        color = "Variant_Classification",
                        palette = palette) +
        theme_Publication() + 
        labs(title = paste(cgGFAC_id[i])) +
        scale_y_continuous(limits = c(0, ylim)) +
        theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)))
}
```

# What type of alterations we observe per tumor descriptor in each cancer group (add _n))?
 

```{r plot-n, fig.width = 12, fig.height = 8, fig.fullwidth = TRUE}
cg <- as.character(unique(df_plot$cg_id))
cg

# Loop through variable
for (i in seq_along(cg)){
  print(i)
  df_sub <- df_plot %>%
      filter(cg_id == cg[i])
  
  # Create bxp
  print(ggpubr::ggboxplot(df_sub, 
                        x = "tumor_descriptor_cg_n", 
                        y = "log10_tmb", 
                        color = "Variant_Classification",
                        palette = palette) +
        theme_Publication() + 
        xlab("Timepoint") +
        labs(title = paste(cg[i])) +
        scale_y_continuous(limits = c(0, ylim)) +
        theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)))
}
```


# What type of alterations we observe per tumor descriptor in each cancer group and timepoint model?

```{r plot-timepoint-model, fig.width = 25, fig.height = 18, fig.fullwidth = TRUE}
tm <- as.character(unique(df_plot$timepoints_models))
tm

# Loop through variable
for (i in seq_along(tm)){
  print(i)
  df_sub <- df_plot %>%
      filter(timepoints_models == tm[i])
  
  # Create bxp
  print(ggpubr::ggboxplot(df_sub, 
                        x = "tumor_descriptor", 
                        y = "tmb", 
                        color = "Variant_Classification",
                        palette = palette) +
        facet_wrap(~cancer_group) +
        theme_Publication() +
        ylab("TMB") +
        xlab("Timepoint") +
        scale_y_continuous(limits = c(0, ylim)) +
        theme(axis.text.x = element_text(angle = 90)))
}
```


```{r echo=TRUE}
sessionInfo()
```
